The vision of future electronic marketplaces (e-markets) is that of markets being populated by autonomous intelligent entities—software, trading, e-agents—representing their users or owners and conducting business on their behalf. For this vision to materialize, one fundamental issue that needs to be addressed is that of trust. First, users need to be able to trust that the agents will do what they say they do. Second, they need to be confident that their privacy is protected and that the security risks involved in entrusting agents to perform transactions on their behalf are minimized. Finally, users need to be assured that any legal issues relating to agents trading electronically are fully covered as they are in traditional trading practices. In this paper we consider the barriers for the adoption of agent technology in electronic commerce (e-commerce) which pertain to trust, security and legal issues. We discuss the perceived risks of the use of agents in e-commerce and the fundamental issue of trust in this context. Issues regarding security, and how some of these can be addressed through the use of cryptography, are described. The impact of the use of agent technology on the users' privacy and how it can be both protected as well as hindered by it is also examined. Finally, we discuss the legal issues that arise in agent-mediated e-commerce and discuss the idea of attributing to software agents the status of legal persons or e-persons and the various implications.

After setting the performance benchmarks for image, video, speech and audio processing, deep convolutional networks have been core to the greatest advances in image recognition tasks in recent times. This raises the question of whether there are any benefit in targeting these remarkable deep architectures with the unattempted task of recognising human rights violations through digital images. Under this perspective, we introduce a new, well-sampled human rights-centric dataset called Human Rights Understanding (HRUN). We conduct a rigorous evaluation on a common ground by combining this dataset with different state-of-the-art deep convolutional architectures in order to achieve recognition of human rights violations. Experimental results on the HRUN dataset have shown that the best performing CNN architectures can achieve up to 88.10% mean average precision. Additionally, our experiments demonstrate that increasing the size of the training samples is crucial for achieving an improvement on mean average precision principally when utilising very deep networks.

Web personalization systems are used to enhance the user experience by providing tailor-made services based on the user's interests and preferences which are typically stored in user profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the users' changing behaviour. In this paper, we introduce a set of methods designed to capture and track user interests and maintain dynamic user profiles within a personalization system. User interests are represented as ontological concepts which are constructed by mapping web pages visited by a user to a reference ontology and are subsequently used to learn short-term and long-term interests. A multi-agent system facilitates and coordinates the capture, storage, management and adaptation of user interests. We propose a search system that utilizes our dynamic user profile to provide a personalized search experience. We present a series of experiments that show how our system can effectively model a dynamic user profile and is capable of learning and adapting to different user browsing behaviours.

Identifying potential abuses of human rights through imagery is a novel and challenging task in the field of computer vision, that will enable to expose human rights violations over large-scale data that may otherwise be impossible. While standard databases for object and scene categorisation contain hundreds of different classes, the largest available dataset of human rights violations contains only 4 classes. Here, we introduce the 'Human Rights Archive Database' (HRA), a verified-by-experts repository of 3050 human rights violations photographs, labelled with human rights semantic categories, comprising a list of the types of human rights abuses encountered at present. With the HRA dataset and a two-phase transfer learning scheme, we fine-tuned the state-of-the-art deep convolutional neural networks (CNNs) to provide human rights violations classification CNNs (HRA-CNNs). We also present extensive experiments refined to evaluate how well object-centric and scene-centric CNN features can be combined for the task of recognising human rights abuses. With this, we show that HRA database poses a challenge at a higher level for the well studied representation learning methods, and provide a benchmark in the task of human rights violations recognition in visual context. We expect this dataset can help to open up new horizons on creating systems able of recognising rich information about human rights violations. Our dataset, codes and trained models are available online at https://github.com

The web services paradigm has enabled an increasing number of providers to deploy and host autonomic and remotely accessible services. However, the true potential of such a distributed infrastructure can only be reached when such autonomic services can be combined together as parts of a workflow, in order to collectively achieve combined functionality. In this paper we present our work in the area of automatic workflow composition among web services with semantically described functionality capabilities. For that purpose, we are using a set of heuristics derived from the connectivity structure of the service repository in order to effectively guide the composition process. The methodologies described in this paper have been inspired by research in areas such as citation analysis and bibliometrics. In addition, we present comparative experimentation results in order to evaluate the presented techniques.